Visualizing Covid-19 data with Plotly

world_map

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Activate the necessary libraries

# to build the country input field
from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widget

# to build dataframes 
import pandas as pd
import numpy as np

Import data from Johns Hopkins CSSE

death_df = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
confirmed_df = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
recovered_df = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
country_df = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv")

Data cleaning - set column names to lower case

death_df.columns = map(str.lower, death_df.columns)
confirmed_df.columns = map(str.lower, confirmed_df.columns)
recovered_df.columns = map(str.lower, recovered_df.columns)
country_df.columns = map(str.lower, country_df.columns)

Data cleaning - renaming columns

death_df = death_df.rename(columns = {"province/state":"state","country/region":"country"})
confirmed_df = confirmed_df.rename(columns = {"province/state":"state","country/region":"country"})
recovered_df = recovered_df.rename(columns = {"province/state":"state","country/region":"country"})
country_df = country_df.rename(columns = {"country_region":"country"})

Data cleaning - remove rows where the location (i.e. latitude) is not available

death_df=death_df[death_df["lat"].notna()]
confirmed_df=confirmed_df[confirmed_df["lat"].notna()]
recovered_df=recovered_df[recovered_df["lat"].notna()]
country_df=country_df[country_df["lat"].notna()]

Visualise the dataframes

death_df.head()

	state	country	lat	long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	7/10/21	7/11/21	7/12/21	7/13/21	7/14/21	7/15/21	7/16/21	7/17/21	7/18/21	7/19/21
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	5561	5561	5791	5791	5923	5983	5983	5983	5983	6213
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	2456	2456	2456	2456	2456	2456	2456	2456	2456	2456
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	...	3824	3836	3851	3867	3882	3895	3910	3922	3938	3956
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	...	127	127	127	127	127	127	127	127	127	127
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	...	929	935	939	941	946	951	952	954	957	969

5 rows × 549 columns

confirmed_df.head()

	state	country	lat	long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	7/10/21	7/11/21	7/12/21	7/13/21	7/14/21	7/15/21	7/16/21	7/17/21	7/18/21	7/19/21
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	131586	131586	134653	134653	136643	137853	137853	137853	137853	141489
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	132587	132592	132597	132608	132616	132629	132647	132665	132686	132697
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	...	145296	146064	146942	147883	148797	149906	151103	152210	153309	154486
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	...	14075	14075	14155	14167	14167	14239	14273	14273	14273	14359
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	...	39881	39958	40055	40138	40327	40530	40631	40707	40805	40906

5 rows × 549 columns

recovered_df.head()

	state	country	lat	long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	7/10/21	7/11/21	7/12/21	7/13/21	7/14/21	7/15/21	7/16/21	7/17/21	7/18/21	7/19/21
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	80226	81077	82586	82586	82586	82586	82586	82586	82586	82586
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	130047	130050	130052	130053	130059	130061	130067	130072	130074	130081
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	...	100911	101430	102010	102594	103192	103809	104397	104992	105604	106337
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	...	13770	13770	13787	13796	13796	13829	13844	13844	13844	13897
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	...	34298	34327	34357	34500	34584	34620	34724	34732	34755	34790

5 rows × 549 columns

country_df.head()

	country	last_update	lat	long_	confirmed	deaths	recovered	active	incident_rate	people_tested	people_hospitalized	mortality_rate	uid	iso3
0	Afghanistan	2021-07-20 09:21:04	33.93911	67.709953	141489.0	6213.0	82586.0	52690.0	363.460133	NaN	NaN	4.391154	4	AFG
1	Albania	2021-07-20 09:21:04	41.15330	20.168300	132697.0	2456.0	130081.0	160.0	4611.057057	NaN	NaN	1.850833	8	ALB
2	Algeria	2021-07-20 09:21:04	28.03390	1.659600	154486.0	3956.0	106337.0	44193.0	352.297208	NaN	NaN	2.560750	12	DZA
3	Andorra	2021-07-20 09:21:04	42.50630	1.521800	14359.0	127.0	13897.0	335.0	18584.093703	NaN	NaN	0.884463	20	AND
4	Angola	2021-07-20 09:21:04	-11.20270	17.873900	40906.0	969.0	34790.0	5147.0	124.461956	NaN	NaN	2.368846	24	AGO

Sort the country_df for the highest to the lowest confirmed cases

sorted_country_df = country_df.sort_values("confirmed", ascending = False)

sorted_country_df.head()

	country	last_update	lat	long_	confirmed	deaths	recovered	active	incident_rate	people_tested	people_hospitalized	mortality_rate	uid	iso3
181	US	2021-07-20 09:21:04	40.000000	-100.00000	34132079.0	609231.0	NaN	NaN	10359.809413	NaN	NaN	1.784922	840	USA
79	India	2021-07-20 09:21:04	20.593684	78.96288	31174322.0	414482.0	30353710.0	406130.0	2259.001663	NaN	NaN	1.329562	356	IND
23	Brazil	2021-07-20 09:21:04	-14.235000	-51.92530	19391845.0	542756.0	17356265.0	1492824.0	9123.023578	NaN	NaN	2.798888	76	BRA
62	France	2021-07-20 09:21:04	46.227600	2.21370	5934122.0	111682.0	408973.0	5413467.0	9091.163962	NaN	NaN	1.882031	250	FRA
144	Russia	2021-07-20 09:21:04	61.524000	105.31880	5931925.0	147457.0	5318062.0	466406.0	4064.787028	NaN	NaN	2.485820	643	RUS

Highlighting the columns of interest

def highlight_col(x):
    r = "background-color: #FF9AA2"
    p = "background-color: #E0BBE4"
    g = "background-color: #CCCCCC"
    temp_df = pd.DataFrame("", index = x.index, columns = x.columns)
    temp_df.iloc[:,4] = p
    temp_df.iloc[:,5] = r
    temp_df.iloc[:,6] = g
    return temp_df

sorted_country_df.head().style.apply(highlight_col, axis = None)

	country	last_update	lat	long_	confirmed	deaths	recovered	active	incident_rate	people_tested	people_hospitalized	mortality_rate	uid	iso3
181	US	2021-07-20 09:21:04	40.000000	-100.000000	34132079.000000	609231.000000	nan	nan	10359.809413	nan	nan	1.784922	840	USA
79	India	2021-07-20 09:21:04	20.593684	78.962880	31174322.000000	414482.000000	30353710.000000	406130.000000	2259.001663	nan	nan	1.329562	356	IND
23	Brazil	2021-07-20 09:21:04	-14.235000	-51.925300	19391845.000000	542756.000000	17356265.000000	1492824.000000	9123.023578	nan	nan	2.798888	76	BRA
62	France	2021-07-20 09:21:04	46.227600	2.213700	5934122.000000	111682.000000	408973.000000	5413467.000000	9091.163962	nan	nan	1.882031	250	FRA
144	Russia	2021-07-20 09:21:04	61.524000	105.318800	5931925.000000	147457.000000	5318062.000000	466406.000000	4064.787028	nan	nan	2.485820	643	RUS

Visualising worse hit countries in a scatter plot

import plotly.express as px

fig = px.scatter(sorted_country_df.head(10),
                x = "country",
                y="confirmed",
                size="confirmed",
                color="country",
                hover_name="country",
                size_max=60)

fig.show()

Visualising data of each country over time

import plotly.graph_objects as go

def plot_cases_for_country(country):
    labels = ["confirmed","deaths"]
    colors = ["#E0BBE4","#FF9AA2"]
    mode_size = [6,8]
    line_size = [4,5]
    
    df_list = [confirmed_df, death_df]
    
    fig = go.Figure()
    
    for i, df in enumerate(df_list):
        if country == "World" or country == "world":
            x_data = np.array(list(df.iloc[:, 5:].columns))
            y_data = np.sum(np.asarray(df.iloc[:, 5:]), axis=0)
            
        else:
            x_data = np.array(list(df.iloc[:, 5:].columns))
            y_data = np.sum(np.asarray(df[df["country"]==country].iloc[:,5:]), axis=0)
            
        fig.add_trace(go.Scatter(x=x_data, y=y_data, mode="lines+markers",
                                name=labels[i],
                                line=dict(color=colors[i], width=line_size[i]),
                                connectgaps=True,
                                text = "Total" + str(labels[i]) + ": " + str(y_data[-1])
                                ))
        
    fig.show()

interact(plot_cases_for_country, country="World")

<function __main__.plot_cases_for_country(country)>

Visualising data of each country on a world map

import folium

world_map = folium.Map(location=[11,0], tiles="cartodbpositron", zoom_start=2, max_zoom=6, min_zoom=2)

for i in range(len(confirmed_df)):
    folium.Circle(
    location=[confirmed_df.iloc[i]["lat"], confirmed_df.iloc[i]["long"]],
    fill=True,
    radius = (int((np.log(confirmed_df.iloc[i,-1] + 1.00001))) + 0.2)*50000,
    fill_color="blue",
    color="red",
    tooltip = "<div style='margin: 0; background-color: black; color: white;''>"+
                    "<h4 style='text-align:center;font-weight: bold'>"+confirmed_df.iloc[i]["country"] + "</h4>"
                    "<hr style='margin:10px;color: white;''>"+
                    "<ul style='color: white;;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
                        "<li>Confirmed: "+str(confirmed_df.iloc[i,-1])+"</li>"+
                        "<li>Deaths:   "+str(death_df.iloc[i,-1])+"</li>"+
                        "<li>Death Rate: "+ str(np.round(death_df.iloc[i,-1]/(confirmed_df.iloc[i,-1]+1.00001)*100,2))+ "</li>"+
                    "</ul></div>"
    ).add_to(world_map)

world_map

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Future directions

• Possibility of deploying the application on Heroku, so that the data can be updated automatically everytime the raw data is updated.
• Possibility of creating an interactive chart showing the past and active the clusters in Singapore, and the corresponding location.

References

• Tutorial by Harshit
• Plotly documentation